Latest news with #RomanElsohvili
Forbes
7 days ago
- Business
- Forbes
The Future Of Banking Apps: How AI Is Reshaping Development
Roman Elsohvili is the Founder and CEO of XData Group, a B2B software development company with a focus on the European banking sector. The digital banking market continues to boom. Dimension Market Research has projected that it will reach $31.3 billion by 2033. However, growth doesn't just happen by itself. The real champion behind this story is technology—more specifically, AI. AI is consistently gaining a greater impact on banking and fintech. It's fundamentally changing how applications are being developed, services are delivered and risks are managed. Let's take a closer look at how this works and what challenges are still ahead. The AI-Powered Toolbox: What's Driving Real Change? Ask any fintech founder or bank CTO about where AI is making the biggest difference today, and a few clear answers are likely to come up—customer-facing chatbots, document generation, compliance automation and so on. In onboarding, for example, AI can help automate document checks and ID verification, drastically cutting down the time it takes to bring in new clients. In compliance and transaction monitoring, AI can perform retrospective analysis, flagging unusual behavior even if it doesn't fit preprogrammed patterns. This helps reduce false positives, which have long been a plague for compliance teams. In customer service, AI-driven chatbots powered by large language models can handle routine queries, support customers 24/7 and often outperform human agents in both speed and consistency. In credit scoring, machine learning (ML) models can evaluate a much broader range of data points, including alternative sources like mobile phone usage or utility bills. This results in faster, more accurate lending decisions and improved risk management. When it comes to fraud prevention, AI is essential. With bad actors also using AI tools to improve their tactics, the industry has no choice but to adopt this tech to match the rising threat level. Why Banks And Fintechs Must Invest In Better Apps Let's be honest: If your app isn't good, you're not in the game. Today, the mobile or web application is the main gateway between a financial service provider and its customers, and users' expectations are very high. They compare your app not to the bank next door but to the best experience they've ever had—often from agile fintechs that put a lot of focus on smart, intuitive interfaces. It's no surprise that players stuck with legacy systems are feeling the pressure. According to OutSystems' State of Application Development Report, nearly half of surveyed financial institutions cited outdated technology as their top innovation barrier, and over half reported that a lack of skilled developers was holding them back. As a result, this is also a space where AI can play a prominent role. How AI Is Reshaping The Way Applications Are Built Setting aside customer-facing features, AI is changing the very architecture of fintech applications. Today, more and more companies are moving to design their applications with AI in mind from day one. Data flows are structured to feed ML models, and the architecture is set up to integrate with internal or third-party ML services. This makes it easier to build smart features directly into the app, from personalized financial advice to automated document review. For developers, AI is also a powerful productivity booster. From writing code snippets to generating test cases, it speeds up workflows and helps smaller teams ship faster. Tools are already available on the market that make it possible to build and deploy models without deep AI expertise. There's also a strategic element to consider here. AI models are becoming more affordable, and leading companies are already planning features that may not be cost-effective today but likely will be in just a few months. It's a smart way to future-proof the roadmap. Not All Smooth Sailing Of course, AI adoption comes with challenges—technical, regulatory and ethical. The biggest hurdle is data. Training robust AI models requires large, high-quality datasets. Established companies might have access to years of support chat transcripts or billions of transactions, but younger fintechs often don't. That's a tough gap to bridge. There's also the issue of regulation. With Europe's AI Act and other global frameworks emerging, fintechs and banks are under pressure to ensure transparency and accountability in how their AI makes decisions. "Black box" systems just won't cut it—especially in compliance and AML, where regulators need to understand the rationale behind every flagged transaction. Security is another concern. When using external APIs or non-self-hosted AI models, protecting sensitive financial and customer data becomes even more critical. Once again, explainability is a must—not just for regulators but for internal teams and end users as well. The good news is that there are ways to tackle these challenges. Addressing The Challenges Proactively First, responsible AI design starts with anticipating regulatory demands. The smarter fintech companies are already building explainability and transparency into their AI systems, documenting decision making steps and auditing model performance to detect bias or drift. Techniques like explainable AI (XAI), which generate human-readable justifications for decisions, are becoming more common. These might look like simple cause-and-effect summaries that show why a transaction was flagged or why a loan was denied. In high-stakes use cases like compliance or AML, many companies still leave the final decision to a human and use AI as a decision-support tool rather than a full replacement. The road ahead is still long, and we shouldn't expect AI to solve every problem a business has to deal with, but it's already solving many of them. For banks and fintechs willing to experiment, invest and learn quickly, the payoff can be huge—smarter applications, happier customers, faster development and better compliance. In a market where your app is your handshake, storefront and sales pitch all in one, using AI to make it better isn't just an option. It's the path forward. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
18-04-2025
- Business
- Forbes
Tech-Driven Digital Banking: Innovations And Challenges Ahead
Roman Elsohvili is the Founder and CEO of XData Group, a B2B software development company with a focus on the European banking sector. The digital banking industry is growing at a rapid pace these days, with projections putting it at over $32 billion in size by 2035. For comparison, in 2024, this market stood at $10 billion. So we're talking here about a growth of $20-plus billion within a decade—not bad at all. Of course, the question then becomes: What's driving this kind of rocket growth? The answer: A lot of it comes down to a fundamental shift in consumer behavior driven by smartphone usage. With each passing day, more and more people go digital, relying on mobile devices and internet access for all kinds of needs, financial and otherwise. Faced with this reality, making banking digital and taking it online is a necessity rather than a luxury. According to a survey by Manole Capital Management, an overwhelming number of Gen-Z users (96%) show a preference for online banking services. This generation expects a seamless digital experience, which means banks must prioritize technological innovation to stay relevant as time goes on. So what are the must-have tech features that will shape digital banking in the coming years? Let's take a look. Artificial intelligence (AI) is a hot topic these days, and it's not hard to see why it can be leveraged to deliver more personalized user experiences than ever before. There's data out there indicating that global AI-driven sales reached a very impressive $229 billion in the last months of 2024. This highlights the immense impact of AI on people's spending habits. Given the ever-growing prevalence of platforms that offer financial services, banks should always look for ways to build customer loyalty if they want to stay ahead of the competition. And AI is certainly one way of doing so, since it allows them to anticipate user needs and offer targeted, relevant solutions. By analyzing patterns of customer behavior, AI can proactively recommend products and services that suit individual needs. Beyond personalization, AI also plays a crucial role in automating operations and enhancing fraud detection, meaning that banks can conduct transactions for customers faster and with greater safety. The next big technological driver we need to talk about is cloud-based banking solutions. Banks can leverage these to streamline development, reducing the costs of infrastructure upkeep and improving its flexibility. Cloud computing fosters collaboration across fintech platforms, allowing established banks to integrate new solutions faster and more efficiently. Through cloud-native APIs, they can integrate third-party services without the burden of having to overhaul their entire legacy systems. This means that new products can be tested and deployed for customer use faster. In an industry where user demands are constantly evolving, this kind of flexibility is very useful to have. Instead of trying to build every financial service in-house, banks can integrate with fintech partners to offer a diverse suite of solutions from which customers can benefit. While technological advancements are playing a major role in shaping the future of digital banking, it would be remiss of me not to speak of the challenges they bring. Banks (and other financial organisations, as well) have to learn to navigate these carefully. • Cybersecurity Threats: As digital banking becomes more sophisticated, so do cyber threats. This is where a major problem with AI comes in. While highly useful in fraud prevention, it can also be used by cybercriminals to develop more advanced attack strategies. I expect that dealing with this threat will lead to an AI arms race, where banks will have to continuously improve their security frameworks to stay ahead of hackers. • Regulatory Gaps: The rapid pace of technological advancements often means that regulators struggle to keep up and make new rules to account for the changes. Ensuring compliance with the law while updating their systems is going to be a challenge for financial companies. They will have to work closely with regulatory agencies worldwide in order to develop best practices that are flexible yet secure enough to support growth without compromising consumer protection. • Legacy System Integration: Many traditional banks still operate on outdated systems, making digital transformation a complex and costly endeavor. Migrating to more modern solutions requires a lot of time, effort and money. And yet, progress waits for no one—failing to adapt will lead to inefficiencies and an inability to meet evolving consumer demands, causing such banks to be left behind. • Talent Shortages: The demand for specialists in AI and fintech in general is constantly running ahead of the available supply. According to market estimates, the global AI talent pool is expected to reach 1.08 million by 2026, but the actual demand for such professionals may surpass 2 million before the end of 2025. This obvious talent gap presents a major barrier for banks looking to implement AI-driven solutions. The digital banking sector has a long road ahead of it, and there are still many critical challenges that banks will have to address before they're able to innovate effectively. But those among them that manage to successfully overcome these obstacles will not only thrive but also set the standard for the future of digital finance. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
